{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ***Introduction to Radar Using Python and MATLAB***\n",
    "## Andy Harrison - Copyright (C) 2019 Artech House\n",
    "<br/>\n",
    "\n",
    "# Diffraction\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Referring to Section 2.8, diffraction is the mechanism by which waves spread out when passing through an aperture or around the corner of an object and into the *shadow region*.  While diffraction may be caused by many objects and geometries in a given scenario, the diffraction discussed here is caused by the earth.  \n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Begin by getting library path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import lib_path"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set the radar and target latitude (degrees), longitude (degrees) and altitude (km)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "radar = {'lat': 26.5, 'lon': 97.0, 'alt': 1.0 * 1e3}\n",
    "\n",
    "target = {'lat': 31., 'lon': 96.0, 'alt': 13.0 * 1e3}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set up the frequencies to be used (Hz) using the `linspace` routine from `scipy`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from numpy import linspace\n",
    "\n",
    "frequency_start = 1e6\n",
    "\n",
    "frequency_end = 300e6\n",
    "\n",
    "frequency = linspace(frequency_start, frequency_end, 1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set the material conductivity and relative permittivity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "relative_permittivity = 1.3\n",
    "\n",
    "conductivity = 0.01"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate the diffraction pattern"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from Libs.wave_propagation import diffraction\n",
    "\n",
    "gamma = [diffraction.attenuation(radar, target, f, relative_permittivity, conductivity) for f in frequency]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Display the diffraction using the routines from `matplotlib`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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urDv44VtP4D2vewamEuFBH9bAaGpvs1DAh6un4rh6Kt7y85WqwULaqQ2Oa3lcXi3gfCqL+UwRj82lccdjCyhuu6zHeDS4bQWyvoV1MobjE1FMxIJW/AdHK/7s68X2erG9Xja054DoMdd1EQ7bPSQVihX82mcfw19+9TxumEngk//XS3DrqUODPqyB09CeAL9PNl6vCNT+3KfTaYyN1VYkq1WDVM6trzo2r0AWcH45h698K4XcttdBxkL+LauOjcGxMVAeToQ5QA4x/uzrxfZ6sb1eNrTngOixYrE46EPoq0evpPGOj9+Hp5dy+InbrsHPvvYmldtJW7G9PbXX3N7nE8wkI5hJRvDcqyZ33NcYg/VCCZdW68PjWgGXVvMbQ+R9F9awXtj6+oZQ/XIeG0PkRBQnDm2+FnJ2LAL/Ac/ESt3jz75ebK8X2+tlQ3sOiNQzf/fAZbznUw9hPBrE37ztRXjp9dODPiSikSMimIiFMBFrfzbWrFuuvQZyNY/La/XXQ9ZfE/nYY4tIZd0t9/f7BEfHI1tWIJtfD3l0PLrnmViJiIhIBw6IHrNhX/J25UoVv/bZx/GnXzqLF546hD/8kefhcHK0l9b7wcb21Jlet0+EA7hpNombZltfE9IpVTYGx+0rkF99ahkL6cuobjsT60wyXB8gm7avNp1IJ67gUjT9wp99vdheL7bXy4b2/C++x4rF4shfG6VZoVjBOz52H/7l8UXc/h2n8N7veaaqS1fsh23tqXNet48E/bjucALXHW79H6lSpYr5dQcXmwbHxq8PXVrD5745h1Jl65lYJ2PBjWGxsXV182Q6UYxHeSKddvizrxfb68X2etnQngOix2zYl9ywXijhbX/5DZw+v4pf+oFb8KMvvnrQhzTUbGpP+zNs7YP+3a8HWa0aLGZcXF7Lb7wOsjFAPr2Uw11PplAobT2RTjzk3xwgJ7cOkScno5hOhOFT+jrIYetP3mF7vdheLxvac0CkriymHfzYn92Dp5ay+P03PRevf/axQR8SEfWIzyeYHY9gdjyC57f4dx9jDFbzpY3rP24fIu+/uIa1/LYT6fh9ODYRabsKOTse4e4DIiKiIcAB0WPxeOtrp42SVNbFm/7b1zC37uDPb38hbruBJ6PphA3tqTu2tRcRHIqHcCgewrNO7H4inca1IC81DZB3PrGExczWE+n4BJgdi7RdhTwxGR3ZMyLb1p86x/Z6sb1eNrTngOixcrk86EM4kLV8EW/+06/j8loBf/nWF+JF104N+pBGxqi3p+5pbN/JiXTm1p2WQ+Tp86v4zENzqFS3vg5yOhFqGh6jO06qMx4NevHU9k1jf6phe73YXi8b2nNA9JjrukgmW/8P07ArFCu4/c+/gadTOXz0LbdyONynUW5PB8P2O0WCflwzHcc1063/pbVcqWIh424ZIGtnZC3g8fkM/uWxRbjl6pavSYYDG5fuaLUKOZ0IDeREOuyvF9vrxfZ62dCeAyJ1pFI1+E//8348eGkNf/zm5+M7bzg86EMiIosF/L6NVULg0I7PG2OQyhabXvu4dYj8+tkVZJyt/4obDvi2bFndPkTOjkXgV3oiHSIiogYOiB4b1X3Jv/qPj+Hzjyzg/a+/Ga/9ttlBH85IGtX2dHBs33sigsPJMA4nw3jOyYmW91kvlJou45Gv/VofKO+YSyOV3XqmOb9PMDsWqQ2P9etAHtvyFkEstP//bLK/XmyvF9vrZUN7Dogeq1are99pyPzv+y/hT790Fm95ydX48ZeeGvThjKxRbE+9wfaDMR4NYjwaxM3Hxlp+vlCsbBkam1chv/bUMubTDra9DBKTseDGwHi8PjQ2f3y4xeU82F8vtteL7fWyoT0HRI8VCoWR+peFx+fT+PlPP4wXXnMI73/9zbwQ9gGMWnvqHbYfTtGQH9fPJHD9TKLl50uVKhbSDubWHVypD5JX1gq4subg4koeX3t6ecc21qC/domQY+O1gfHoRARjgQpuPHG4PlBGkQjzP71a8GdfL7bXy4b2/K8UtZVxSvi///o+JCNB/MF/eC4CvEYZESkS9PtwYjKGE5OxtvdJOyXMrW0fIGtD5NfPrmA+7dTPxnpu42vGIoGmFcjN7auNj2eSYf59S0REA8MB0WOxWPv/0Rg2H/r7R3F+OYeP/8cXYyYZGfThjLxRak+9xfb2GosEMTYbbHs5j0rV4PziKlZd4HJ9kGy8XV5zcPr8KtYLpS1f03gtZPP21dpAufnxWGQ4L+lBW/FnXy+218uG9hwQqaXPPzKPT913Ce985fW8nAURUZcaw961sRief3Xr++TcMubWC1sGyMZq5P0X1vCPD8+hVNn6YshkOLCx8ti8CnlsvPb+7HgEQa5CEhFRFzggeiyfzw/9vyyksi5+4dMP45bjY3jHK28Y9OFYYxTaU3+wvW579Y+HA7h+JonrZ1qvQlarBqmsWx8at21nXS/gwUvrWMltPSOrCHAkuXUV8uh4pP5We23kdHznCXWot/izrxfb62VDew6ItMMH/+4RZNwyPv7Dz0EowH+BJiIaJJ9PMDMWwcxYBM+9qvV9CsUKrqxvfQ1kY4D85uV13PHoAtzy1jPrBf2CI2O1VcejE/XBsT5ENgbKQ/EQT05GRKQMB0SPRaPRQR/Cru58YhH/8PAcfuY1N+LGI63/NZu6M+ztqX/YXjcv+kdDflx3OIHrDrc+I6sxBqv5Eq6sFTC37mBuvf7rWgFX1h3cd2EV8+s7t7KGAr7NobE+SM6OR3GsvhJ5bCKC8WiQQ2Qb/NnXi+31sqE9B0SP+XzDuyLnlCr44N8/gmsPx/EfX3btoA/HOsPcnvqL7XUbhv4igkPxEA7FQ7jl+HjL+1SrBsu5IubWayuQc+sFzK87uFIfJLeelXVTNOivDZFbViFrw+Sx8drrIcciAZVD5DC0p8Fge71saM8B0WO5XG5o/2Xhj+58CueX8/ibt70I4YB/0IdjnWFuT/3F9rqNSn+fT3A4GcbhZBjPPtH6PpX66yEbK5FX1mpD5Ny6gyvrBXzpTAqLGQfbZkjEQ34c3fY6yGPNA6Wl14cclfbUe2yvlw3t7fvbmLpyZa2AP/riU/jebz+Gl14/PejDISKiIeT31V63eGQsgue2uU+5UsVixt2yElnbzlp7//H5DFJZF2bbEJmMBDZWHJuHx2MTURwZqw2WcQuHSCKiYTM0f9OKyDsA3A7gWQA+boy5velzNwP47wCuq990L4B3GmMe9fgwDywcDg/6EFr6nTueBAzwntfdNOhDsdawtqf+Y3vdtPUP+H0bZ05td2mPYrmKhbSz8XrIK2sO5tdrr4ecq59YZ3nbmVmB2hA5OxbB7HgEs/WhcXY8itnxMGbHasPlZGx4XhOprT1tYnu9bGg/NAMigCsAPgLgtQC2r8teAfBGAOcB+AD8JID/AeDZXh5gLwQCw/RbXvPEfAafuu8Sfvyl1+DE5GiflneYDWN78gbb68b+O4UCPpw8FMPJQ+3/m+OUKlhIO7iy5mwMk/PrBcynHcyvO3hyIYPFzM6VyMaJdRqrjo2BcvO2KA4nw/B7cIkPtteL7fWyof3QPANjzKcBQERuBXBi2+fWAKzVPy8AKgCu9/oYe2EY9yX/5ucfRzwUwE9+10j+lo6MYWxP3mB73di/O5GgH1dPxXH1VLztfcqVKpaybn14rL+lN9+//8Ia5tcdFCtbL/HhE2AmubkSOTu+OUQ2Pj4yFkEkeLDX47O9Xmyvlw3th2ZA7ISIrAFIoLaK+IEBH44V7ruwin9+bBE/+9qbMBkPDfpwiIiIOhbw++qvVWz/P2PGGKzkipuDY3rrMPnUUhZf/lYKGbe842snY8HaFtaxMGbrr4lsHihnxyNIhnWeoZWI7DVSA6IxZkJE4gDegtp205ZE5O0A3g4AJ06cgOM4KBaLKBZrr2eIx+Mol8twXXfj42q1ikKhAACIxWpbXvL5PIDa9Ux8Ph9yuRyA2t7iQCCw8XEoFEIoFEI2m934OBwOI5vNwhiDYDCIcDiMfD6PXC6HQCCASCSCfD6ParUKn8+HWCwGx3FQLpc3PnZdF6VSCSKCRCIB13U3nkMikejJc/qdzz+K8UgAb3z2NAqFQlfPqVqtDtVz6kWnfjynXC5n3XOysVM/nlOpVILjOFY9Jxs79fM5pVIp657TKHWKRyI4GqngSMiP5x5JIhY7suM5raRzuLKax2K2hHRZcGklh/m1AhayRSysO3jw4hpW8iVsFwv66ifuCWEqGsBMMoSTUwnMJMMIV/K4KlfCsakkAn4/Oyl6TsP2/3vsxP/f2w8x2zfv94GI3Ang5W0+/WVjzG1N9/0IgBPNJ6lp8Xg+AEsAnmmMWdzte996663m9OnT+z7mfnEcB5FIZNCHAQB4fD6N1/3u3XjXq27ET73qhkEfjvWGqT15i+11Y397uOUKFtPullXIufXGayQLWEi7WEg7KG+7zkfAJ5hJhnFkPIIjyQiOjG2+X9vOGsbMGFcjbcKfe72Gtb2I3GuMubWT+3qygmiMeUWPH9IHIAbgOIBdB8Rhk81mh+YPzR/d+RTiIT/e8h1tTjNHPTVM7clbbK8b+9sjHPDveXKdatUglXNrJ9K5tIi8CWEh7WB+3cVipral9StPpZB2dm5pjYX89dXI8MblRBofz9bfnxkL81rFI4A/93rZ0H5otpiKSAC14/ED8ItIBEDZGFMWkVcDSAF4CEActbOdrgJ4bFDHO+ouLOfxmQev4G3feS0mYnztIRERUS/4fIKZZAQzyQiORcqYnm59beF8sYzF+orjfNrZWJlcqL/df2EN82kHxXJ1x9dOxoI7hseZ+sez9dumEt6cqZWI7DM0AyKA9wH4YNPHbwbwYQAfAjAB4PdRO7tpAcA3ALzOGON4fIwHFgoNxzD2Z18+i4DPh5+47ZpBH4oaw9KevMf2urG/Xru1j4UCODUdwKnp9mdpNcZgvVDCQtPwuFgfKBvbWR+fT2Mp42Lbrlb4fYLDifCW1cjZ8Uhtq2vjTK3JCMai3NbaD/y518uG9kMzIBpjPoTaMNjqc58A8Akvj6dfhuHimTm3jE/dewnf8+yjODI22kvgo2QY2tNgsL1u7K/XQduLCCZiIUzEQrhpNtn2fuVKFcu5Yn0rq4OFjIuF+msjFzIuzi/ncc+5Fay1OMlOpHGSnWSk/rrIcG2QHNt8vxeX/NCGP/d62dB+aAZELbLZ7MD/4PzdA1eQcct484v52kMvDUN7Ggy214399fKqfcDv21glfPaJ9vdzSpUdW1kX6quR82kHD19awx1pB05p57bWZCSwsfo4k6ydVGfLr/XPxcP8X0uAP/ea2dCeP8Ue8+KssXt9///+1XO4+egYnnfVxECPRZtBt6fBYXvd2F+vYWsfCfpx1VQMV021P8mOMQZpp7xjK+ti2sFixsVixsW9F1axkHZbvj4yHvJjZiyCw83DZDKMmbEwjiRrJ9k5nIxgLGL31tZha0/esaE9B0SPBYPBgX7/e8+v4vH5DH71Dc+y+i/mYTTo9jQ4bK8b++s1iu1FBOPRIMajQdxwpP22VmMM0oUyFjO1wXGhMUCmXSxkHCylXTx8aQ0LaReFUmXH10eCvvrJfGrD40x9eGzc1hguJ2LBkfz/lVFsT71hQ3sOiB4b9JLzX33tPJLhAL7/OccGehwaDbo9DQ7b68b+etncXkQwHgtiPLb3IJl1yxvD42LG2fy1ftvj8xnc/WQKGXfnpT9Cfh8ObwyRWwfIw2ObW1sPxULwDdFZW21uT7uzoT0HRI/l8/mBXRsl45TwuW/O44dvPYlYiOm9Nsj2NFhsrxv768X2tUEyGQkiGQniusOJXe+bL5axlHGx0DRINlYjFzMuzqZy+NrTK1gv7DzZTsAnmE5sX42svX+k6bapeAgBv69fT3fzubC9Wja055TgsWp15359r3zum/Nwy1X84POOD+wYNBtkexostteN/fVi+/2JhQK4eiqAq6faX/oDqJ1sZ6n+esjN10Y2BkoXl1bzuP/CKpZzxR1fKwJMxUOYToRxOFl7m0lGNt4/3HT7QV4nyfZ62dCeA6LHAoHB/Zb/7QOXcfVUDM89yZPTDMIg29Ngsb1u7K8X2/dHJOjHyUOuuVxXAAAgAElEQVQxnDzU/mQ7AFAsV5HKbg6SCxkXqfpguZRxsZR18fRSDksZF8XKzv+pDwd8LQfH7UPldCKEcGDrZUDYXi8b2o/+Mxgxg1pynl938JWnlvHOV94wki/2tsGobzeg7rG9buyvF9sPVijgw7GJKI5NRHe9X/MJdxqD41Jm822xfi3J0+dXsdJiVRIAxqPB+uBYGxonowEcnVjeMVRORIND9VpJ6j0bfu45IHpsUPuS//7ByzAG+IHncnvpoNiwJ526w/a6sb9ebD8aOj3hDgCUKlUsZ4v1wdHZHCSbhsr7L6xhMe3AaXEZkMZrJZtXJmuX/ti5SsnzRYwmG37u+SfPY4Pal/y/77+Cbz85gWumd9/XT/1jw5506g7b68b+erG9fYJ+H2bHI5gdjwAYb3u/paUlRMcmm1YhnS2rkkvZ2qVBHr68juWsi2qLS+dtXFNy2+DY/PF0IoypRAhBD068Q52x4eeeA6LHfD7vf4CfWsrisbk0PvD6mz3/3rRpEO1pOLC9buyvF9vr5ff7kQgHkAgH9vzH+UrVYCVX3LISuX2gfGw+jbvOuMg4Oy8FAgCTsSCmE7WBcbr+usjpRG2YnE5unpRnKh5GKMA/l/1kw889B0SPxWK7v6C6H/7pkQUAwGtvmfX8e9OmQbSn4cD2urG/Xmyv137a+32ysSK4l0KxsnHinaWMi1R28632cREPXVpDKuMiV6y0fIzxaHBjgJyur0hOJ0IbK5LNQ+b2k+/Q3mz4ueeA6DHHcTzfl3zHo/O45fgYju/xIm3qr0G0p+HA9rqxv15sr1e/2kdDnZ3BFdgcJpeytbO3prLFpkGy9vbolTRSGRcZt/XKZDISqA+QtZXIw1sGyKaVymQYkSCHScCOn3sOiB4rl1v/APbLUsbF/RfX8J+++0ZPvy/t5HV7Gh5srxv768X2eg1D+/0Mk06pUh8ai/VhsnmQLGIp6+Lx+Qy+lEkh3WabayIc2Ljsx8ZKZNMW1+YtrzafgGcY2h+UvXWGlNf7kv/lsQUYA7z65iOefl/ayYY96dQdtteN/fVie71GrX0k6MeJyRhOTO49TLrlCpbrq5GprItUprjx2snGbWcWs/jq08tYy5daPkY85N+xCrm55TVUP/lO7QQ8yXBgpC7RNmrtW+GA6DGv9yXf8egCjk9E8cyju5+2mfrPhj3p1B2214399WJ7vWxuHw74O7q+JAAUy1Ws5IobW103hsjM5oB5NpXDN86tYjVfhGlxNtdQwIfpeGhjYJyK14bKjfeTYUzFa0PloXho4CfhsaE9B0SPua7r2b7knFvG3d9K4T+88KqR+pcXW3nZnoYL2+vG/nqxvV5sXxMKNF8WZHflSm2YXMq6WM4WsZyr/brxcdbFcq6IMwtZLGVdFFtcZxIAxiKBjct/1AbI5qGyNkxO1Vcux6PBnv8/sg3tOSB6rFRqvdTeD1/6VgrFchWv4fbSoeBlexoubK8b++vF9nqx/f4F/D7MjEUwM7b3cGWMQdYtbwySqWxxyxBZGypdPLWUxT3nim1XJwM+2RgkpxpbW+OhLauSU02DZScn4rGhPQdEj3m5knfXk0uIh/x4wTWHPPue1B5XcfVie93YXy+214vt+0tEkIwEkYwEcWqP60wCtdXJ1XwJqabVyVRjoGy8njJXxNlUDqmsC6fUenUyEQ60XImcatoCGywXMDZRHfhW14PggOixRCLh2fe6+0wKL7luCkH/6P4BtYmX7Wm4sL1u7K8X2+vF9sMl4Pd1fK1JAMgXyy23t6aaBsrzy3ncd2EVK7kiqttWJ//5pydw/czonv+DA6LHXNdFONzZH86DOL+cw4WVPH7itmv6/r2oM161p+HD9rqxv15srxfbj7ZYKIDYoUBHlwipVA3W8sWNAfLi4lpHJ/AZZhwQPVYsFj35PnefSQEAbrth2pPvR3vzqj0NH7bXjf31Ynu92F4Pv0/q20vDuPFIEqlxjPx1Hrn30FJfOpPC8Ykoru1gXzYRERERERHAAdFzXuxJr1YNvvr0Ml56/RRfJD1E+HoEvdheN/bXi+31Ynu9bGjPAdFjXmw5eGIhg/VCCS++dqrv34s6x+0merG9buyvF9vrxfZ62dCeA6LHvPhDc8/ZFQDAC3l5i6Fiw18Y1B2214399WJ7vdheLxvac0C00D1nV3B8IooTk3ufeYmIiIiIiKiBA6LH4vH+njTGGIOvn13h6uEQ6nd7Gl5srxv768X2erG9Xja054DosXK53NfHP5vKIZV1OSAOoX63p+HF9rqxv15srxfb62VDew6IHnNdt6+P33j94QtOcUAcNv1uT8OL7XVjf73YXi+218uG9hwQLXP/hTWMR4O47vDoL28TEREREZG3OCB6rN/7kh+4uIbnnJzg9Q+HkA170qk7bK8b++vF9nqxvV42tOeA6LFqtdq3x844JTy5mMFzTk707XtQ9/rZnoYb2+vG/nqxvV5sr5cN7TkgeqxQKPTtsR++tA5jgOdcxQFxGPWzPQ03tteN/fVie73YXi8b2nNAtMj9F9cAAM85wQGRiIiIiIj2jwOix2Kx/l28/oGLa7hmOo7JeKhv34O618/2NNzYXjf214vt9WJ7vWxozwHRIo0T1BAREREREXWDA6LH8vl8Xx53Me1gKePiluPjfXl8Orh+tafhx/a6sb9ebK8X2+tlQ3sOiJZ45EoaAPBtx8YGfCRERERERDSqOCB6LBqN9uVxH52rDYg3c0AcWv1qT8OP7XVjf73YXi+218uG9hwQPebz9ee3/JEr67jqUAxjkWBfHp8Orl/tafixvW7srxfb68X2etnQfvSfwYjJ5XJ9edxHrqS5vXTI9as9DT+214399WJ7vdheLxvac0C0QNop4fxyngMiEREREREdCAdEj4XD4Z4/5uNzGQB8/eGw60d7Gg1srxv768X2erG9Xja054DosUAg0PPHfPTKOgDg247xEhfDrB/taTSwvW7srxfb68X2etnQngOix/qxL/mJhSwmYkHMJEf/XyxsZsOedOoO2+vG/nqxvV5sr5cN7TkgWuDMQgY3ziQhIoM+FCIiIiIiGmEcED0WCoV6+njGGDy5kMENRxI9fVzqvV63p9HB9rqxv15srxfb62VDew6IHuv1H5rFjIu0U8aNR5I9fVzqPRv+wqDusL1u7K8X2+vF9nrZ0J4Dosey2WxPH+/JhdoZTLmCOPx63Z5GB9vrxv56sb1ebK+XDe05II64Jxdqfwi5gkhERERERAfFAdFjvV52PrOQwaF4CNMJnsF02Nmw5YC6w/a6sb9ebK8X2+tlQ3sOiB7r9cUzn1zI4IYZbi8dBTZcOJW6w/a6sb9ebK8X2+tlQ3sOiB7r5b5kYwzOLGa5vXRE2LAnnbrD9rqxv15srxfb62VDew6IHjPG9OyxUtkiMk4Z1x6O9+wxqX962Z5GC9vrxv56sb1ebK+XDe05IHosGAz27LHOLecAANdMc0AcBb1sT6OF7XVjf73YXi+218uG9hwQPdbLfclnlzggjhIb9qRTd9heN/bXi+31Ynu9bGjPAdFj+Xy+Z491djmHgE9wfCLas8ek/ullexotbK8b++vF9nqxvV42tOeA6LFqtdqzxzqXyuGqqRgCfmYcBb1sT6OF7XVjf73YXi+218uG9pwsPBYIBHr2WGdTOVwzxe2lo6KX7Wm0sL1u7K8X2+vF9nrZ0J4DoscikUhPHqdaNTi3nMMpvv5wZPSqPY0etteN/fVie73YXi8b2nNA9Fiv9iXPpx04pSpPUDNCbNiTTt1he93YXy+214vt9bKhPQdEj/VqX/K5FM9gOmps2JNO3WF73dhfL7bXi+31sqE9B0SP+Xy9+S0/W78GIreYjo5etafRw/a6sb9ebK8X2+tlQ/vRfwYjJhaL9eRxzixkEQ36cXRs9Pc5a9Gr9jR62F439teL7fVie71saM8B0WOO4/TkcR65so5vOzYGn0968njUf71qT6OH7XVjf73YXi+218uG9hwQPVYulw/8GNWqwSNX0rjl+HgPjoi80ov2NJrYXjf214vt9WJ7vWxozwHRY73Yl3x2OYd8sYKbj4314IjIKzbsSafusL1u7K8X2+vF9nrZ0H70n8GI6cW+5DMLWQDAM2aTB34s8o4Ne9KpO2yvG/vrxfZ6sb1eNrTngOgx13UP/BiXVmvXV7nq0Oj/AdSkF+1pNLG9buyvF9vrxfZ62dCeA6LHSqXSgR/j4koeyXAA49FgD46IvNKL9jSa2F439teL7fVie71saM8B0WMiBz/r6MXVAk4civXkscg77KUX2+vG/nqxvV5sr5cN7TkgeiyRSBz4MS6u5HFyMtqDoyEv9aI9jSa214399WJ7vdheLxvac0D02EH3JRtjcGm1gJN8/eHIsWFPOnWH7XVjf73YXi+218uG9hwQPVYsFg/09cu5IgqlClcQR9BB29PoYnvd2F8vtteL7fWyoT0HxBFzcaV2BtMTk1xBJCIiIiKi3uKA6LGD7ku+uFoAAG4xHUE27Emn7rC9buyvF9vrxfZ62dCeA6LHDrrsvLmCyC2mo8aGLQfUHbbXjf31Ynu92F4vG9pzQPTYQf/QXFrNYyoeQjwc6NERkVds+AuDusP2urG/XmyvF9vrZUN7Dogj5uJK7RqIREREREREvcYB0WPxePxAX395rYATE9xeOooO2p5GF9vrxv56sb1ebK+XDe05IHqsXC53/bXGGFxZK+DYRKSHR0ReOUh7Gm1srxv768X2erG9Xja054DosYNcPHMlV4RbruIYVxBHkg0XTqXusL1u7K8X2+vF9nrZ0J4D4giZW3cAAEfHOSASEREREVHvcUD02EH2JV9eq10D8ThXEEeSDXvSqTtsrxv768X2erG9Xja054DosWq12vXXXqkPiEf5GsSRdJD2NNrYXjf214vt9WJ7vWxozwHRY4VCoeuvnVt3EAr4MBUP9fCIyCsHaU+jje11Y3+92F4vttfLhvYcEEfI5bUCjk9EISKDPhQiIiIiIrIQB0SPxWLdX+R+bq2Ao+PcXjqqDtKeRhvb68b+erG9Xmyvlw3tOSCOkCtrDi9xQUREREREfTNUA6KIvENETouIKyJ/scv9PigiRkRe5eHh9UQ+n+/q60qVKhYzDo5xBXFkddueRh/b68b+erG9Xmyvlw3tA4M+gG2uAPgIgNcCaLlUJiLXAXgjgDkPj2vgFtIOqgZcQSQiIiIior4ZqhVEY8ynjTF/C2B5l7v9AYD3ACh6c1S9FY12N+DNrTsAgKMcEEdWt+1p9LG9buyvF9vrxfZ62dB+qAbEvYjIvwVQNMb846CPpVs+X3e/5Y1rIB7nNRBHVrftafSxvW7srxfb68X2etnQfti2mLYlIgkAvwLgNR3c9+0A3g4AJ06cgOM4KBaLKBZri47xeBzlchmu6258XK1WN65b0jj7UGMPcTQahc/nQy6XAwCEw2EEAoGNj0OhEEKhELLZ7MbH4XAY2WwWxhgEg0GEw2Hk83msrq5iamoKkUgE+Xwe1WoVPp8PsVgMjuOgXC5vfOy6LkqlEkQEF1L1xy7nkUq5SCQSQ/OcqtUqAoHAvp9TIpGA67obz8H257S+vo5jx45Z9Zxs7NSP55TP5zEzM2PVc7KxU7+e08LCAhKJhFXPycZO/XhOc3NzGB8ft+o52dhpmP5/b5ifk42dNP3/3n6IMWZfX9AtEbkTwMvbfPrLxpjbmu77EQAnjDG3N9322wDWjTG/WP/4HIC3GWP+ebfve+utt5rTp08f7OB7KJVKYXp6et9f9/6//Sb+/sErePCDe87HNKS6bU+jj+11Y3+92F4vttdrWNuLyL3GmFs7ua9na6DGmFcYY6TN2217PwK+G8A7RWReROYBnATwv0TkPf098t4Kh8Ndfd3cOq+BOOq6bU+jj+11Y3+92F4vttfLhvZDtcVURAKoHZMfgF9EIgDKxpgyagNisOnu3wDw0wA+6/mBHkAg0N1v+eU1B8d5gpqR1m17Gn1srxv768X2erG9Xja0H7ZXUb4PQAHAzwF4c/399wGAMWbZGDPfeANQAbBqjMkO7Gi70NhLvF9z6wUc5QlqRlq37Wn0sb1u7K8X2+vF9nrZ0H6oRlxjzIcAfKjD+57q57EMk3yxjLV8CUfHuYJIRERERET9M2wriNYLhUL7/pqFdO0MSHwN4mjrpj3Zge11Y3+92F4vttfLhvYcED3W3YDoAACOjHFAHGU2/IVB3WF73dhfL7bXi+31sqE9B0SPNa5fsh+bA+LonxVJs27akx3YXjf214vt9WJ7vWxozwFxBCzWt5jOcAWRiIiIiIj6iAOix7rdYhoN+pEMD9U5hWifbNhyQN1he93YXy+214vt9bKhPQdEj3Vz8cyFjIuZsTBEpA9HRF6x4cKp1B2214399WJ7vdheLxvac0D0WDf7khfTDo4kub101NmwJ526w/a6sb9ebK8X2+tlQ3sOiB4zxuz7axbrK4g02rppT3Zge93YXy+214vt9bKhPQdEjwWDwX3d3xiDhbTDS1xYYL/tyR5srxv768X2erG9Xja054Dosf3uS866ZeSLFV7iwgI27Emn7rC9buyvF9vrxfZ62dCeA6LH8vn8vu6/UL/EBVcQR99+25M92F439teL7fVie71saM8B0WPVanVf919MOwCAGZ6kZuTttz3Zg+11Y3+92F4vttfLhvYcED0WCOzvWoYLmdqAyC2mo2+/7ckebK8b++vF9nqxvV42tOeA6LFIZH8rgYv1LaYz3GI68vbbnuzB9rqxv15srxfb62VDew6IHuvmNYjxkB+J8Oj/a4R2NuxJp+6wvW7srxfb68X2etnQngOix/a7L3kp6+JwkttLbWDDnnTqDtvrxv56sb1ebK+XDe05IHrM59vfb3kq42I6wQHRBvttT/Zge93YXy+214vt9bKh/eg/gxETi8X2df/lnIupRKhPR0Ne2m97sgfb68b+erG9Xmyvlw3tOSB6zHGcfd0/lS1yBdES+21P9mB73dhfL7bXi+31sqE9B0SPlcvlzu9bqWI1zwHRFvtpT3Zhe93YXy+214vt9bKhPQdEj+1nX/JKrghjgGluMbWCDXvSqTtsrxv768X2erG9Xja0H/1nMGL2sy85lS0CAFcQLWHDnnTqDtvrxv56sb1ebK+XDe05IHrMdd2O75vK1u47zctcWGE/7ckubK8b++vF9nqxvV42tOeA6LFSqdTxfRsD4lScW0xtsJ/2ZBe214399WJ7vdheLxvac0D0mIh0fN/lxhZTriBaYT/tyS5srxv768X2erG9Xja054DosUQi0fF9U1kXoYAPyXCgj0dEXtlPe7IL2+vG/nqxvV5sr5cN7Tkgemw/+5KXsi6m4yEr/iWC7NiTTt1he93YXy+214vt9bKhPQdEjxWLxY7vu5wtcnupRfbTnuzC9rqxv15srxfb62VDew6IQyyVdXmJCyIiIiIi8gwHRI/t9zWIPIOpPWzYk07dYXvd2F8vtteL7fWyoT0HRI91uuxsjOEWU8vYsOWAusP2urG/XmyvF9vrZUN7Doge6/QPzXqhhHLVcAXRIjb8hUHdYXvd2F8vtteL7fWyoT0HxCG1nKtfA5GvQSQiIiIiIo9wQPRYPB7v6H5r+dqAOBEL9vNwyEOdtif7sL1u7K8X2+vF9nrZ0J4DosfK5XJH91vNlQAAkzFuMbVFp+3JPmyvG/vrxfZ6sb1eNrTngOixTi+euVJfQeSAaA8bLpxK3WF73dhfL7bXi+31sqE9B8Qh1dhiOhnnFlMiIiIiIvIGB0SPdboveTVfQsAnSIQDfT4i8ooNe9KpO2yvG/vrxfZ6sb1eNrTngOixarXa0f3W8kVMxEIQkT4fEXml0/ZkH7bXjf31Ynu92F4vG9pzQPRYoVDo6H6ruRImeQZTq3TanuzD9rqxv15srxfb62VDew6IQ2olX8RknCeoISIiIiIi7+z5AjcROQLgNQC+HcAEgDUADwK4wxgz39/Ds08sFuvofmv5Iq6ZHv09zLSp0/ZkH7bXjf31Ynu92F4vG9q3XUEUkWeKyCcBPArgRwEEAczXf/1RAI+IyCdF5GZPjlSZ1XyJl7ggIiIiIiJP7baC+BcAfhPAjxhjdlzQQ0RCAL4fwEcBvKQvR2ehfD6/578sGGM2TlJD9uikPdmJ7XVjf73YXi+218uG9m0HRGPMi3b7QmNMEcAn6m/UQ7liBaWK4UlqiIiIiIjIU12dpEZEDvX6QLSIRqN73mc1VwQAnqTGMp20JzuxvW7srxfb68X2etnQftcBUUT8IvJOEfljEXmLiCRE5MsAUiJyWUSe79FxWsPn23smX83XB0RuMbVKJ+3JTmyvG/vrxfZ6sb1eNrTf6xn8HoDbAawDeBeAzwH4MoBbAPwVgN/q58HZKJfL7Xmf1XwJALjF1DKdtCc7sb1u7K8X2+vF9nrZ0H6vy1y8AcCzjDEpEfn/AFwE8N3GGFdEPghgru9HqNBafQWRJ6khIiIiIiIv7bWCGDfGpADAGHMFQLpxRtP6r3teR5G2CofDe95n4zWIXEG0SiftyU5srxv768X2erG9Xja033PAExEB0Hgz2z6mfQoE9p6pV/MliADjUQ6INumkPdmJ7XVjf73YXi+218uG9nutICYAlAGUABQBTDR9XAIQ7+vRWaiz1yAWMRYJIuAf/Re50iYb9qRTd9heN/bXi+31Ynu9bGi/14h7jSdHQVus5UuY4PZSIiIiIiLy2K4DojHmvFcHokUotPeJZ9JOidtLLdRJe7IT2+vG/nqxvV5sr5cN7dsOiCLyVwDMXg9gjPmxnh6R5Tr5Q5NxykhGRn//Mm1lw18Y1B2214399WJ7vdheLxva7/Yit28BeKr+tg7gBwD4AVyqf933A1jr9wHaJpvN7nmfdKGEsQhXEG3TSXuyE9vrxv56sb1ebK+XDe3bLlMZYz7ceF9EPg/ge4wxdzfddhuA9/f38HRKOxwQiYiIiIjIe52eJvPFAL627bavA3hJbw/HftxiqpcNWw6oO2yvG/vrxfZ6sb1eNrTvdEC8H8CviEgUAOq//jKAB/p1YLba6+KZpUoV+WIFYzxJjXVsuHAqdYftdWN/vdheL7bXy4b2nQ6ItwN4KYB1EVlA7TWJtwHgCWr2aa99yRmnDAAY4wqidWzYk07dYXvd2F8vtteL7fWyoX1HU4gx5hyA7xCRkwCOAZgzxlzo54HZypjdTwybcUoAgCRfg2idvdqTvdheN/bXi+31Ynu9bGi/22UuQsaYYvNtxpiLAC5uu1/YGOP26fisEwzuPvilC/UVRG4xtc5e7clebK8b++vF9nqxvV42tN9ti+lDIvJuETnW6pMiclRE3o3a6xOpQ3vtS26sIHKLqX1s2JNO3WF73dhfL7bXi+31sqH9bgPibQBmADwoIk+KyGdE5GP1X59A7QQ1UwBe5sWB2iKfz+/6+TS3mFprr/ZkL7bXjf31Ynu92F4vG9rvdh3EFICfEZFfAPAiAM8CMAFgFcCvAbjHGFPy5CgtUq1Wd/385hZTriDaZq/2ZC+214399WJ7vdheLxva7zmF1F+HeHf9jQ4oENj9t7yxgsjXINpnr/ZkL7bXjf31Ynu92F4vG9p3epkL6pFIJLLr59NOGSJAIjT6f7hoq73ak73YXjf214vt9WJ7vWxozwHRY3u+BrFQQiIcgM8nHh0RecWGPenUHbbXjf31Ynu92F4vG9pzQPTYXvuSM04ZYzxBjZVs2JNO3WF73dhfL7bXi+31sqE9B0SP+Xy7/5annRKSvMSFlfZqT/Zie93YXy+214vt9bKhfUfPQER+WkSeU3//xSJyQUSeFpGX9Pfw7BOLxXb9fLpQ4glqLLVXe7IX2+vG/nqxvV5sr5cN7Tsdcd8F4Gz9/V8F8F8A/DKA3+3HQdnMcZxdP1/bYsoVRBvt1Z7sxfa6sb9ebK8X2+tlQ/tOJ5FxY8y6iCQBfDuAVxljKiLy2308NiuVy+VdP592SnhGJOnR0ZCX9mpP9mJ73dhfL7bXi+31sqF9pwPiRRH5DgDfBuCu+nA4BqDSv0Oz0177kjNOmVtMLWXDnnTqDtvrxv56sb1ebK+XDe07HRB/FsAnARQB/FD9ttcDuKcfB2Wz3fYlV6sGGZ6kxlo27Emn7rC9buyvF9vrxfZ62dC+oxHXGPOPxphjxphTxph76zd/AsD39e/Q7OS6btvP5YplVA14mQtL7dae7Mb2urG/XmyvF9vrZUP7Ts9ierOIHKm/nxCRDwP4eQCcZPapVCq1/VzGqe1ZHotyBdFGu7Unu7G9buyvF9vrxfZ62dC+002yHwMwUX//twC8DMBLAPxJPw7KZiLS9nONATER5txto93ak93YXjf214vt9WJ7vWxo3+lS1SljzBNSe8Y/iNrJagrYvPQFdSiRSLT9XK5YGxDjYb9Xh0Me2q092Y3tdWN/vdheL7bXy4b2na4guvVLXLwQwEVjTAqACyDStyOz1G77kvNu7aSw8TC3mNrIhj3p1B2214399WJ7vdheLxvadzqJfAzAFwAkAfxB/bbngSuI+1YsFtt+rrGCGAtxBdFGu7Unu7G9buyvF9vrxfZ62dC+owHRGPMuEXkNgJIx5l/rN1cBvKtvR6ZQvrHFNMQVRCIiIiIi8l7Hk4gx5p9E5CoReQmAy8aY0308Lmvtti85W99iGuNrEK1kw5506g7b68b+erG9Xmyvlw3tO73MxVER+SKAMwA+DeBbIvJFETnW16Oz0G7LznmXK4g2s2HLAXWH7XVjf73YXi+218uG9p2epOaPADwI4JAx5iiASQAPAPjjfh2YrXZ/DWJtBTEa5AqijWz4C4O6w/a6sb9ebK8X2+tlQ/tOl6puA3DUGFMCAGNMTkTeDeBy345MobxbRjzkh883+tdPISIiIiKi0dPpCuIqgKjqEjwAACAASURBVJu33XYTgLXeHo794vF428/lihXEeIkLa+3WnuzG9rqxv15srxfb62VD+06nkd8A8M8i8lEA5wFcDeCtAN7frwOzVblcbvu5fLG2gkh22q092Y3tdWN/vdheL7bXy4b2Ha0gGmP+G4B/B2AawPfWf32TMea/9vHYrLTbxTNzbgUxnqDGWjZcOJW6w/a6sb9ebK8X2+tlQ/v9XObiCwC+0PhYRPwi8ovGmA/05cgUyhfLiPMSF0RERERENCCdvgaxlQCA9/bqQABARN4hIqdFxBWRv9j2uVMiYkQk2/Q2cltc93wNIlcQrWXDnnTqDtvrxv56sb1ebK+XDe0POo30+nSbVwB8BMBrAUTb3GfCGDOym3ur1Wrbz+XdMo5PRDw8GvLSbu3JbmyvG/vrxfZ6sb1eNrQ/yAoiAJieHEXjwYz5tDHmbwEs9/Jxh0mhUGj7uTxXEK22W3uyG9vrxv56sb1ebK+XDe13nUZE5JW7fDrU42Pp1HkRMQDuAPCzxpjUgI6j57Iuz2JKRERERESDs9dy1Uf3+PyFXh1IB1IAXgDgAQBTAP4QwN+gth11CxF5O4C3A8CJEyfgOA6KxSKKxSKA2t7gcrm8cZaheDyOarW6MfHHYjEAQD6fBwBEo1H4fD7kcjkAQDgcRiAQ2Pg4FAohFAohm81ufBwOh5HNZmGMQTAYRDgcRj6fh+M4WFtbQyQSQT6fR7Vahc/nQywWQ84tQypFrKysIBaLwXVdlEoliAgSiQRc1914DolEYmieU7VaRSAQaPmcHMdBuVze+Fjzc3IcB4VCwarnZGOnfjynarUKx3Gsek42durXc6pUKkilUlY9Jxs79eM5OY6DVCpl1XOysZPX/783qs/Jxk6a/n9vP8SYnu4Sbf+NRO4E8PI2n/6yMea2pvt+BMAJY8ztuzzeLIA5AOPGmHS7+916663m9OnTXR1zP+Tz+Y0/FM2K5SpufN9n8TOvuRHveOUNAzgy6rd27cl+bK8b++vF9nqxvV7D2l5E7jXG3NrJfQ/6GsSOGWNeYYyRNm+37f0IOx+y/muvT5TTV41/Kdhxe7F23h2+BtFe7dqT/dheN/bXi+31Ynu9bGg/VNOIiARQOyY/AL+IRACUjTFlEXkRgDUAZwBMAvg9AHcaY9YHdsA9lCtWAIDXQSQiIiIiooHxbAWxQ+8DUADwcwDeXH//ffXPXQvgcwAyAL4JwAXwpgEc44FEo62v3pF3uYJou3btyX5srxv768X2erG9Xja0H6ppxBjzIQAfavO5jwP4uJfH0w/tXiTKFUT77fcFwmQPtteN/fVie73YXi8b2u/rGYjIjIhc2/zWrwOzVeNsRNs1VhDjXEG0Vrv2ZD+214399WJ7vdheLxvadzSNiMjrULvkxSy2nhTGoPZ6QTqgzRVEDohERERERDQYna4g/iGAXwKQMMb4mt44HO5TOBxuefvmWUz5W2qrdu3JfmyvG/vrxfZ6sb1eNrTvdLlqEsCfGK8ummixQKD1b3nO5Qqi7dq1J/uxvW7srxfb68X2etnQvtMVxI8CeGs/D0SLdvuScy5XEG1nw5506g7b68b+erG9Xmyvlw3tOx1xXwzgnSLycwDmmz9hjHlZz49KoVyRl7kgIiIiIqLB6nQa+dP6Gx1QKBRqeXu+WEEk6IPfJy0/T6OvXXuyH9vrxv56sb1ebK+XDe07GhCNMX/Z7wPRot0fmpxb5iUuLGfDXxjUHbbXjf31Ynu92F4vG9p3fB1EEXmriHxBRJ6o/8rXJHYhm822vD1frCAW5usPbdauPdmP7XVjf73YXi+218uG9p1eB/G9AH4MwG8DOA/gagDvFpFjxphf7uPxqZFzy4gFuYJIRERERESD0+lE8jYArzDGnG/cICKfB3AXAA6I+9Bu2blQqiDKM5hazYYtB9QdtteN/fVie73YXi8b2ne6xTQOYGnbbcsAor09HPu1u3imW6oiEux4xy+NIBsunErdYXvd2F8vtteL7fWyoX2nE8nnAPyNiNwkIlEReQaAvwTw+f4dmp3a7UsulCqIBrmCaDMb9qRTd9heN/bXi+31Ynu9bGjf6YD4DgAZAA8CyAJ4AEAOwP/bp+OyljGm5e3cYmq/du3JfmyvG/vrxfZ6sb1eNrTv9DIXaQA/JiK3A5gGkDLGVPt5YLYKBoMtb3dKFUQCHBBt1q492Y/tdWN/vdheL7bXy4b2bQdEETlljDlXf//abZ9OiNQu6G6MebpvR2ehdvuSnVIFEa4gWs2GPenUHbbXjf31Ynu92F4vG9rvtoL4MIBk/f1vATAAZNt9DABONfuQz+cRiUR23O6UqlxBtFy79mQ/tteN/fVie73YXi8b2rcdEI0xyab3eXrNHqlWd+7MNcbUX4PI32abtWpPOrC9buyvF9vrxfZ62dC+o4lERH6vze2/29vDsV8gsHMmL1UMKlXDs5harlV70oHtdWN/vdheL7bXy4b2nS5Z3d7m9h/t0XGo0XJ7ablS+xwHRKuN+nYD6h7b68b+erG9Xmyvlw3tdx1xReTHG/drer/hWgCpvhyVxVrtS3aKHBA1sGFPOnWH7XVjf73YXi+218uG9nutgTZWCEPYulpoACwAeEs/DspmrfYlO6XabRwQ7WbDnnTqDtvrxv56sb1ebK+XDe13HRCNMd8FACLyEWPM+7w5JLv5fDt39RZKtRVEvgbRbq3akw5srxv768X2erG9Xja07/QZ3CUiNzbfICI3icir+3BMVovFYjtu2xgQeRZTq7VqTzqwvW7srxfb68X2etnQvtOJ5A8BZLbdlqnfTvvgOM7O2+oDIq+DaLdW7UkHtteN/fVie73YXi8b2nc6IM4YY+a23TYHYLbHx2O9crm847bGCmIkxAHRZq3akw5srxv768X2erG9Xja073RAfFpEXrnttlcAONvbw7Ffq33JLlcQVbBhTzp1h+11Y3+92F4vttfLhvadXsnxQwA+LSIfBfAUgOsAvLX+Rvuw+2sQOSDazIY96dQdtteN/fVie73YXi8b2nc04hpj/g7AawDEAXxP/dfX1m+nfXBdd8dtjctc8CymdmvVnnRge93YXy+214vt9bKhfacriDDG3APgnj4eiwqlUmnHbYVifYtpcPSXpKm9Vu1JB7bXjf31Ynu92F4vG9p3PCCKyHMAfCeAaQDSuN0Y84E+HJe1RGTHbW65toIY4Qqi1Vq1Jx3YXjf214vt9WJ7vWxo39GSlYi8HcCXAbwSwHsAPAvAfwZwff8OzU6JRGLHbW65toIY8nMF0Wat2pMObK8b++vF9nqxvV42tO90Ink3gNcZY34QQKH+6xsBjP4aqsda7Ut2y1WE/D74fKP/Lw7Ung170qk7bK8b++vF9nqxvV42tN/PdRDvrr9fFRGfMeazAL63T8dlrWKxuOM2t1RFOMDVQ9u1ak86sL1u7K8X2+vF9nrZ0L7T1yBeEpFTxphzAJ4E8P0ikgIw+r8DQ8AtVxDmCWqIiIiIiGjAOh0QfwPAMwGcA/CLAD4JIATgnf05LHu1fg1iFeEAT1BjOxv2pFN32F439teL7fVie71saL/nspXUTsVzF4A7AKC+tXQSwKQx5o/6e3j2abnFtMwtphrYsOWAusP2urG/XmyvF9vrZUP7PacSY4wB8DCAatNtRWNMtp8HZqvWr0GsIMQB0Xo2/IVB3WF73dhfL7bXi+31sqF9p1PJ/QBu7OeBaOaWqwjzGohERERERDRgnb4G8U4AnxORvwBwEYBpfMIY82e9Pyx7xePxHbe55Qq3mCrQqj3pwPa6sb9ebK8X2+tlQ/tOB8SXAjgL4OXbbjcAOCDuQ7lc3nGbW64iEe40BY2qVu1JB7bXjf31Ynu92F4vG9q3nUpE5PuMMX9f//A1xpiSR8dkNdd1kUwmt95WqmIqzi2mtmvVnnRge93YXy+214vt9bKh/W77Gv+66f3lfh+IZrwOIhERERERDYPd9jXOi8g7ADwKICAi3wVAtt/JGPOFfh2cjVq/BpGXudDAhj3p1B2214399WJ7vdheLxva7zYgvhXAhwH8FID/v717D5I1r+s7/vn29FzOzLgiLpKsh90FvKFEpNigBpBNaRWKQUipKbnobkSW0kLiJVYRXcyia1JSMaWpeAkR5CoVEhA1kkQTBcRLdNGsuogkAsuuq+BZ9jbTMz3T/XzzRz89p6d3zmV6Z56n+/d5v6qmtnumz5xfn/f2Oed7nt/z9KqOPtcwJT3hFNZVrKqqHva50YDIFtPSHdUeHmjvjf6+aO+L9r5KaH+xw1YfysyvyczPl/SxzHz8ER8Mh8e0s7PzsM/197mKqYOj2sMD7b3R3xftfdHeVwntLzaV3Dlx++OnvA5ro/dBZEAEAAAA0K6LTSW9iHhyRCxJenqMdKY/mlpoKdbX1w/dz0y2mJqYbg8ftPdGf1+090V7XyW0v9g5iK+R9AcanX8oSdNv6hEanYPIZPMI7A1H+5TZYgoAAACgbRecSjLzZyVdIekaSTsaXYxm8uPx4gI1x9br9Q7d7w8YEF1Mt4cP2nujvy/a+6K9rxLaX+wIojJzIOnuiHhqZt55scdiNv39ekBc5kAsAAAAgHZdcECMiB/KzB+r735rxMPeAlGSlJk/fBoLK9WZM2cO3e8PhpKk1SWOIJZuuj180N4b/X3R3hftfZXQ/mJHEM9O3H7caS/ERadzeBA82GLKVUyLN90ePmjvjf6+aO+L9r5KaH/BATEzv3Pi9j9tZjnl297ePvQvCwdbTDkHsXjT7eGD9t7o74v2vmjvq4T2Fz0HcSwivljSsyQ9WtKnJf12Zn7oNBfmYrzFdIUBEQAAAEDLLjogxujEw9dLukHS3ZLukfS5kq6KiLdI+vbMzFNfZUFWV1cP3d8fjn75Vpa4SE3pptvDB+290d8X7X3R3lcJ7S912OomSddL+orMvCYzvzIzr5b0lRodUXz5Ka+vON3u4Zl8rz4HkSOI5ZtuDx+090Z/X7T3RXtfJbS/1FTyrZJemZl/OPnJ+v731F/HMWxvbx+6vz8cDYjLS0dfJRblmG4PH7T3Rn9ftPdFe18ltL/UgPjFkt53ga+9r/46HoE+RxABAAAAzIlLTSVLmfnQUV+oP89Uc0wrKyuH7o+PIK7wPojFm24PH7T3Rn9ftPdFe18ltL/UJtnliPiHki60/3HxN9k27IIDIkcQi1fCbxiYDe290d8X7X3R3lcJ7S814H1K0hsu8XUcw9bWltbW1g7ujy9Ss8wRxOJNt4cP2nujvy/a+6K9rxLaX3RAzMxrG1qHLY4gAgAAAJgXTCUNmz7s3OcIoo0SthxgNrT3Rn9ftPdFe18ltGcqadj0m2fuD3P0eY4gFq+EN07FbGjvjf6+aO+L9r5KaM9U0rCtra1D9zkH0cd0e/igvTf6+6K9L9r7KqE9U0nDMvPQ/f1hpU5IS50LXSgWpZhuDx+090Z/X7T3RXtfJbRnQGzY8vLyoft7w4oL1JiYbg8ftPdGf1+090V7XyW0ZzJp2PS+5L1BxfZSEyXsScdsaO+N/r5o74v2vkpoz2TSsF6vd+j+3rDiAjUmptvDB+290d8X7X3R3lcJ7ZlMGlZV1aH7+xxBtDHdHj5o743+vmjvi/a+SmjPZNKwbrd76P4+5yDamG4PH7T3Rn9ftPdFe18ltGcyadja2tqh+3tDjiC6mG4PH7T3Rn9ftPdFe18ltGcyadjDzkEcpFYYEC2UsCcds6G9N/r7or0v2vsqoT2TScOm9yXvDSsts8XUQgl70jEb2nujvy/a+6K9rxLaM5k0rNM5/Eu+P6i0yhFEC9Pt4YP23ujvi/a+aO+rhPaL/wwWzPr6+qH7oyOI0dJq0KTp9vBBe2/090V7X7T3VUJ7BsSG7e7uHrq/P6w4B9HEdHv4oL03+vuivS/a+yqhPZNJwwaDwaH7e7wPoo3p9vBBe2/090V7X7T3VUJ7JpOGTe9L5iI1PkrYk47Z0N4b/X3R3hftfZXQfvGfwYJ52DmIXKTGRgl70jEb2nujvy/a+6K9rxLaM5k0rN/vH7q/P2SLqYvp9vBBe2/090V7X7T3VUJ7JpOG7e/vH74/TK2wxdTCdHv4oL03+vuivS/a+yqhPZNJwyIOv6UFF6nxMd0ePmjvjf6+aO+L9r5KaM9k0rDNzc1D93kfRB/T7eGD9t7o74v2vmjvq4T2DIgNO+ocRN4H0UMJe9IxG9p7o78v2vuiva8S2jOZNGxvb+/g9rBKZUrdAi6Hi0ubbA8vtPdGf1+090V7XyW0ZzJp0f6wkiR1l9hiCgAAAKB9DIgNm9yXPKhSkrTMgGihhD3pmA3tvdHfF+190d5XCe0ZEBs2edh5MD6CyBZTCyVsOcBsaO+N/r5o74v2vkpoPzeTSUS8IiJui4h+RLzxiK+vR8TPRMS5iHggIt7fwjIfscn/afaHHEF0UsJvGJgN7b3R3xftfdHeVwntu20vYMI9km6V9BxJZ474+us0Wu+TJH1a0pc1t7TTMajG5yDOzZwOAAAAwNjcDIiZ+S5JiojrJJ2d/FpEfKGkb5B0NjMfrD/9wWZXeDI2NjYObg/qI4jdDkcQHUy2hxfae6O/L9r7or2vEtovyqGrL5d0p6TX1FtM/zQivrHtRc1iMBgc3B5fxXSZI4gWJtvDC+290d8X7X3R3lcJ7efmCOIlnJX0ZEnvlHSVpK+U9GsR8aHM/PPpB0fETZJukqSzZ89qd3dXe3t7B3uCNzY2NBgMDt7IcmNjQ1VVaWdnR5K0vr4uSer1epKkM2fOqNPpaHt7W5K0urqqbrd7cH9lZUUrKyva2to6uL+6uqqtrS1lppaXl7W6uqper6f77rtPw+FQa2truve++yVJuzvb2t3d1e7urgaDgTqdjtbX19Xv97W/v6+I0Obmpvr9/sFz2NzcnJvnVFWVut2u1tbWDu6PnwPP6fxzeuCBB9Ttdot6TiV2Oo3n1Ov1tLy8XNRzKrHTaT2ne++9V/1+v6jnVGKn03hOn/rUp9Tv94t6TiV2Ou2/75XynErs5PT3veOIzDzWD5hFRLxX0rMv8OXfycxnTjz2Vo22kt448bnvlfTjktYzc1B/7lcl/c/M/KmL/dzXXXdd3nbbbY/sCZygc+fO6corr5Qk3XHPA/r6f/cB/dxLnqavffLfaXllOG2T7eGF9t7o74v2vmjva17bR8QHM/O6y3lsI0cQM/P6R/gt/uQk1jEPjjoHkauYeihhTzpmQ3tv9PdFe1+091VC+7k5+S0iuhGxJmlJ0lJErEXEeIB9v6RPSPoX9eOeIel6Sf+jndXOrqqvXCpxFVM3k+3hhfbe6O+L9r5o76uE9vM0mdwsaUfSqyS9pL59syRl5r6k50t6rqQHJP1HSd+WmR9uZ6mzG+89libeB5GrmFqYbA8vtPdGf1+090V7XyW0n5uL1GTmLZJuucjX79Do4jTFOHibC44gAgAAAJgDTCYNG1+1SJL2D7aYcgTRwWR7eKG9N/r7or0v2vsqoT0DYov2B/X7IB7z0rMAAAAAcBqYTBo2fr8TSRpU4y2mHEF0MNkeXmjvjf6+aO+L9r5KaM+A2KL9YX0EkXMQAQAAAMwBJpOGnTlz5uA274PoZbI9vNDeG/190d4X7X2V0J4BsWGdifMNeR9ELx3ONbVFe2/090V7X7T3VUL7xX8GC2Z7e/vgNu+D6GWyPbzQ3hv9fdHeF+19ldCeAbFFgyFHEAEAAADMDyaThq2urh7c5iqmXibbwwvtvdHfF+190d5XCe0ZEBvW7XYPbp/fYkoGB5Pt4YX23ujvi/a+aO+rhPZMJg2b3Jd8fospRxAdlLAnHbOhvTf6+6K9L9r7KqE9A2KL9sdbTLlIDQAAAIA5wIDYsJWVlYPbg2GlbicUwYDoYLI9vNDeG/190d4X7X2V0J4BsWGHBsQq2V5qpITfMDAb2nujvy/a+6K9rxLaMyA2bGtr6+D2/rDiAjVGJtvDC+290d8X7X3R3lcJ7ZlOWjQYcgQRAAAAwPxgQGzY4S2mlbpLJHBRwpYDzIb23ujvi/a+aO+rhPZMJw2bfPPM/WFqmSuY2ijhjVMxG9p7o78v2vuiva8S2jMgNmxyX/JgyBFEJyXsScdsaO+N/r5o74v2vkpoz3TSsMw8uL3PVUytTLaHF9p7o78v2vuiva8S2jMgNmx5efng9oCrmFqZbA8vtPdGf1+090V7XyW0Zzpp2PQ5iBxB9FHCnnTMhvbe6O+L9r5o76uE9gyIDev1ege394eVljkH0cZke3ihvTf6+6K9L9r7KqE900nDqqo6uD2sUl2uYmpjsj280N4b/X3R3hftfZXQngGxYd1u9+D2oEotMSDamGwPL7T3Rn9ftPdFe18ltGdAbNja2trB7YqrmFqZbA8vtPdGf1+090V7XyW0Z0Bs2OS+5NERRBK4KGFPOmZDe2/090V7X7T3VUJ7ppOGcQ6irxL2pGM2tPdGf1+090V7XyW0Z0BsWGfiiOGgSnWCAdFFh6PFtmjvjf6+aO+L9r5KaL/4z2DBrK+vH9weVhVHEI1MtocX2nujvy/a+6K9rxLaMyA2bHd39+D2sEotcZEaG5Pt4YX23ujvi/a+aO+rhPYMiA0bDAYHtzkH0ctke3ihvTf6+6K9L9r7KqE9A2LDps9B5H0QfZSwJx2zob03+vuivS/a+yqh/eI/gwVz+BzE1BIXqbFRwp50zIb23ujvi/a+aO+rhPYMiA3r9/sHt4dVqss5iDYm28ML7b3R3xftfdHeVwntGRAbtr+/f3B7yBZTK5Pt4YX23ujvi/a+aO+rhPYMiA2LiS2lgyrVLWCfMi5PsJ3YFu290d8X7X3R3lcJ7ZlOGra5uXlwe1ilOgX8T4TLM9keXmjvjf6+aO+L9r5KaM+A2LDJfcmDquIcRCMl7EnHbGjvjf6+aO+L9r5KaM+A2LC9vb2D21UlzkE0MtkeXmjvjf6+aO+L9r5KaM+A2KJBVanLgAgAAABgTjAgNmy8L7mqUlVyBNFJCXvSMRvae6O/L9r7or2vEtozIDZsfNh5mClJWuIiNTZK2HKA2dDeG/190d4X7X2V0J4BsWEHA2JVD4hcpMZGCb9hYDa090Z/X7T3RXtfJbRnQGzJeEDkHEQAAAAA84IBsWEbGxuSpMH4CGKHBC7G7eGH9t7o74v2vmjvq4T2TCcNGwwGkjiC6GjcHn5o743+vmjvi/a+SmjPgNiw8ZtnDqpKktRhQLRRwhunYja090Z/X7T3RXtfJbRnQGxJPR9yBBEAAADA3GBAbNj5cxBHEyLvg+ijhD3pmA3tvdHfF+190d5XCe0ZEBtW1YMh5yD6GbeHH9p7o78v2vuiva8S2jMgNmxnZ0fS5FVMGRBdjNvDD+290d8X7X3R3lcJ7RkQW1IxIAIAAACYMwyIDVtfX5d0/ggiW0x9jNvDD+290d8X7X3R3lcJ7RkQWzI8OIJIAgAAAADzgemkYb1eTxJHEB2N28MP7b3R3xftfdHeVwntGRBbMqyvcNRhQAQAAAAwJxgQG3bmzBlJ0rC+Ai5HEH2M28MP7b3R3xftfdHeVwntGRAb1qnPORzURxC5iqmPDueb2qK9N/r7or0v2vsqof3iP4MFs729Len8RWo4guhj3B5+aO+N/r5o74v2vkpoz4DYkvFFajgHEQAAAMC8YEBs2OrqqiRpOOQIoptxe/ihvTf6+6K9L9r7KqE9A2LDut2uJGmY4/dBZEB0MW4PP7T3Rn9ftPdFe18ltGdAbNjDz0EkgYsS9qRjNrT3Rn9ftPdFe18ltGc6acn4HESOIAIAAACYFwyIDVtZWZEkDXmbCzvj9vBDe2/090V7X7T3VUJ7BsSGnR8QR/e5SI2PEn7DwGxo743+vmjvi/a+SmjPgNiwra0tSRxBdDRuDz+090Z/X7T3RXtfJbRnQGzJoOJtLgAAAADMFwbEhp3fYjoaEDsMiDZK2HKA2dDeG/190d4X7X2V0J4BsWHjN88cDDmC6KaEN07FbGjvjf6+aO+L9r5KaM+A2LDxvuQqeZsLNyXsScdsaO+N/r5o74v2vkpoz4DYsKwHw/PnIJLAxbg9/NDeG/190d4X7X2V0J7ppGHLy8uSzp+DyBFEH+P28EN7b/T3RXtftPdVQnsGxIZNn4PIgOijhD3pmA3tvdHfF+190d5XCe0ZEBvW6/UkScP68DPzoY9xe/ihvTf6+6K9L9r7KqE9A2LDqqqSNNqf3AkpggnRxbg9/NDeG/190d4X7X2V0J4BsWHdblfS6BzEDsOhlXF7+KG9N/r7or0v2vsqoT0DYsPW1tYkjbaYdthfamXcHn5o743+vmjvi/a+SmjPgNiw8b7kTGmJI4hWStiTjtnQ3hv9fdHeF+19ldCeAbFh433Joy2mLS8GjSphTzpmQ3tv9PdFe1+091VCewbEhnU6o1/yYcUWUzfj9vBDe2/090V7X7T3VUL7xX8GC2Z9fV3S6CqmvAeil3F7+KG9N/r7or0v2vsqoT0DYsN2d3cl1Rep4RxEK+P28EN7b/T3RXtftPdVQnsGxIYNBgNJ0rASA6KZcXv4ob03+vuivS/a+yqhPQNiw8b7kkdbTFteDBpVwp50zIb23ujvi/a+aO+rhPaL/wwWzHhf8ugqphxBdFLCnnTMhvbe6O+L9r5o76uE9gyIDev3+5I4B9HRuD380N4b/X3R3hftfZXQngGxYfv7+5KkTHEVUzPj9vBDe2/090V7X7T3VUJ7BsSGRX3UcLTFtOXFoFHBEWNbtPdGf1+090V7XyW0n5sBMSJeERG3RUQ/It449bUXR8TWxEcvIjIintbScme2ubkpqd5iyoRoZdwefmjvjf6+aO+L9r5KaD83A6KkeyTdKukN01/IzLdl5ub4Q9J3SfqopD9qeI2P2HhfcmZqqYB/YcDlK2FPOmZDe2/090V7X7T3pIo7zQAAFN5JREFUVUL7btsLGMvMd0lSRFwn6ewlHn6DpDdnZp76wk7Y3t6eJK5i6mjcHn5o743+vmjvi/a+Smg/T0cQL0tEXCPpqyS9ue21PBLDSmwxBQAAADBX5uYI4jF8m6TfzsyPXegBEXGTpJsk6ezZs9rd3dXe3t7BRL+xsaHBYHBwCHhjY0NVVWlnZ0fS+fcv6fV6kqQzZ86o0+loe3tbkrS6uqput3twf2VlRSsrK9ra2jq4v7q6qq2tLWWmlpeXtbq6ql6vp729Pd1///0aDIfKaqhz586p0+lofX1du7u7GgwGB/f7/b729/cVEdrc3FS/3z94Dpubm3PznKqqUrfb1dra2sF9ntPDn9Pe3p52dnaKek4ldjqN5yRJu7u7RT2nEjud1nOqqkrnzp0r6jmV2Ok0ntPe3p7OnTtX1HMqsdNp/n2vpOdUYienv+8dRzSxSzMi3ivp2Rf48u9k5jMnHnurpLOZeeMFvtf/lfSvMvMXLufnvu666/K222473oJP0YMPPqgrrrhCN/7CH+jT23v6lVc889I/CEUYt4cf2nujvy/a+6K9r3ltHxEfzMzrLuexjRxBzMzrT+L7RMQzJF0l6b+cxPdrA+cg+iphTzpmQ3tv9PdFe1+091VC+7nZYhoRXY3WsyRpKSLWJA0yczDxsBskvTMzH2pjjScpU1riHEQAAAAAc2SeLlJzs6QdSa+S9JL69s3jL9YD4z+R9KZWVndCNjY2JI2PILa8GDRq3B5+aO+N/r5o74v2vkpoPzdHEDPzFkm3XOTru5Ie1dR6TstgMDogOky2mLoZt4cf2nujvy/a+6K9rxLaz9MRRAvjqxllJltMzZTwxqmYDe290d8X7X3R3lcJ7RkQW8JFagAAAADMGwbEhh2cg5hShyOIVkrYk47Z0N4b/X3R3hftfZXQngGxYVVVSaq3mDIfWhm3hx/ae6O/L9r7or2vEtozIDZsZ2dHEltMHY3bww/tvdHfF+190d5XCe0ZEFsyrJItpgAAAADmCgNiw9bX1yVJmdISRxCtjNvDD+290d8X7X3R3lcJ7RkQWzLMVIdffQAAAABzhBGlYb1eT5JUcQ6inXF7+KG9N/r7or0v2vsqoT0DYkuqTC1xDiIAAACAOcKA2LAzZ85IqreYcgTRyrg9/NDeG/190d4X7X2V0J4BsWGd+sTDqhIDopkOJ53aor03+vuivS/a+yqh/eI/gwWzvb0tabzFtOXFoFHj9vBDe2/090V7X7T3VUJ7RpSWDLlIDQAAAIA5w4DYsNXVVUmjI4gdLlJjZdwefmjvjf6+aO+L9r5KaM+A2LButytJqlJiPvQybg8/tPdGf1+090V7XyW0Z0Bs2Hhf8rBKLbHF1EoJe9IxG9p7o78v2vuiva8S2jMgtqSq2GIKAAAAYL4wIDZsZWVFUn0OIkcQrYzbww/tvdHfF+190d5XCe0ZEBs2/p9mmKkljiBaKeE3DMyG9t7o74v2vmjvq4T2DIgN29rakjS+SA0DopNxe/ihvTf6+6K9L9r7KqE9A2JLqiq5iikAAACAucKA2DC2mPoqYcsBZkN7b/T3RXtftPdVQnsGxIatrq4qM5VsMbVTwhunYja090Z/X7T3RXtfJbRnQGzY1taWqhzdZkD0UsKedMyG9t7o74v2vmjvq4T2DIgNy0wN6wlxiV99K5nZ9hLQEtp7o78v2vuiva8S2jOiNGx5eVlV/T9Oh3MQrSwvL7e9BLSE9t7o74v2vmjvq4T2DIgNW11dPT8gssXUSgl70jEb2nujvy/a+6K9rxLaMyA2rNfrnd9iyoBopdfrtb0EtIT23ujvi/a+aO+rhPYMiA2rqur8RWrYYmqlqqq2l4CW0N4b/X3R3hftfZXQngGxYd1uV1U13mLa8mLQqG632/YS0BLae6O/L9r7or2vEtozIDZsbW1NwxxfxZQJ0cna2lrbS0BLaO+N/r5o74v2vkpoz4DYsF6vx0VqTJWwJx2zob03+vuivS/a+yqhPQNiw6qq0nhrMgOilxL2pGM2tPdGf1+090V7XyW0Z0BsWKfTmdhi2vJi0KhOh+CuaO+N/r5o74v2vkpov/jPYMGsr69PXKSGI4hO1tfX214CWkJ7b/T3RXtftPdVQnsGxIbt7u5yDqKp3d3dtpeAltDeG/190d4X7X2V0J4BsWGDwUDDiquYOhoMBm0vAS2hvTf6+6K9L9r7KqE9A2LDOp2O6vlQHQZEKyXsScdsaO+N/r5o74v2vkpov/jPYMGsr69PbDFteTFoVAl70jEb2nujvy/a+6K9rxLaMyA2rN/vn99iyjmIVvr9fttLQEto743+vmjvi/a+SmjPgNiw/f3980cQOYRoZX9/v+0loCW090Z/X7T3RXtfJbRnQGxYRGj8/plcxdRL0NsW7b3R3xftfdHeVwntGRAbtrm5qWGOr2La8mLQqM3NzbaXgJbQ3hv9fdHeF+19ldCeEaVh/X6f90E0VcKedMyG9t7o74v2vmjvq4T2DIgN29vbU1UxIDra29trewloCe290d8X7X3R3lcJ7RkQW3BwFVMuUgMAAABgjjAgNmxzc1P1fMgRRDMl7EnHbGjvjf6+aO+L9r5KaM+A2LC9vT2tLXf0xMdsaH1lqe3loEElbDnAbGjvjf6+aO+L9r5KaN9tewFu9vb29NSrr9T/+v7r214KGlbCbxiYDe290d8X7X3R3lcJ7TmCCAAAAACQxIDYuI2NjbaXgJbQ3hftvdHfF+190d5XCe0ZEBs2GAzaXgJaQntftPdGf1+090V7XyW0Z0BsWAlvnonZ0N4X7b3R3xftfdHeVwntGRABAAAAAJIYEBtXwr5kzIb2vmjvjf6+aO+L9r5KaM+A2LCqqtpeAlpCe1+090Z/X7T3RXtfJbRnQGzYzs5O20tAS2jvi/be6O+L9r5o76uE9gyIAAAAAABJDIiNW19fb3sJaAntfdHeG/190d4X7X2V0J4BEQAAAAAgiQGxcb1er+0loCW090V7b/T3RXtftPdVQnsGRAAAAACAJAbExp05c6btJaAltPdFe2/090V7X7T3VUJ7BsSGdTr8kruivS/ae6O/L9r7or2vEtov/jNYMNvb220vAS2hvS/ae6O/L9r7or2vEtozIAIAAAAAJDEgNm51dbXtJaAltPdFe2/090V7X7T3VUJ7BsSGdbvdtpeAltDeF+290d8X7X3R3lcJ7RkQG1bCvmTMhva+aO+N/r5o74v2vkpoz4AIAAAAAJDEgNi4lZWVtpeAltDeF+290d8X7X3R3lcJ7RkQG1bC/zSYDe190d4b/X3R3hftfZXQPjKz7TWcqoj4W0l3tr2OCVdKOtf2ItAK2vuivTf6+6K9L9r7mtf212TmYy7ngcUPiPMmIm7LzOvaXgeaR3tftPdGf1+090V7XyW0Z4spAAAAAEASAyIAAAAAoMaA2LzXtb0AtIb2vmjvjf6+aO+L9r4Wvj3nIAIAAAAAJHEEEQAAAABQY0AEAAAAAEhiQDxxEfHoiPiliNiOiDsj4kUXeFxExI9HxL31x2sjIppeL07WMfrfEhH7EbE18fGEpteLkxERr4iI2yKiHxFvvMRjvzci/iYiHoiIN0TEakPLxCm53P4RcWNEDKde99c3t1KcpIhYjYjX17/XPxQRfxwRX3eRx/PaL8hx+vPaL09EvDUi/joiHoyIj0TEd1zksQv32mdAPHk/LWlP0mMlvVjSz0bElxzxuJskvUDSUyR9qaR/JOnlTS0Sp+Zy+0vSf8rMzYmPjza2Spy0eyTdKukNF3tQRDxH0qskfbWkayU9QdJrTntxOHWX1b/2e1Ov+/ee7tJwirqS7pL0bEmfKenVkt4REddOP5DXfpEuu3+N135Z/rWkazPzCknfIOnWiHja9IMW9bXPgHiCImJD0jdKenVmbmXmByT9iqRvPeLhN0j6icy8OzP/StJPSLqxscXixB2zPwqSme/KzHdLuvcSD71B0usz847MvE/Sj4rX/cI7Rn8UJDO3M/OWzPx4ZlaZ+V8lfUzSw/6SKF77xTlmfxSmfi33x3frjyce8dCFfO0zIJ6sL5A0zMyPTHzudklHHUH6kvprl3ocFsdx+kvS8yLi0xFxR0R85+kvD3PgqNf9YyPis1taD5r31Ig4V29JenVEdNteEE5GRDxWoz8H7jjiy7z2C3eJ/hKv/eJExM9ERE/ShyX9taT3HPGwhXztMyCerE1JD0x97gFJn3EZj31A0ibnIS604/R/h6QnSXqMpJdJ+uGIeOHpLg9z4KjXvXT0/yMoz/slPVnS52i02+CFkn6g1RXhRETEsqS3SXpTZn74iIfw2i/YZfTntV+gzPwujV7Dz5L0Lkn9Ix62kK99BsSTtSXpiqnPXSHpoct47BWStpI3plxkl90/Mz+Umfdk5jAzf1fST0n6pgbWiHYd9bqXjv49AoXJzI9m5sfq7Wh/KulHxOt+4UVER9JbNDr//BUXeBiv/UJdTn9e++Wq/x73AUlnJR21G2whX/sMiCfrI5K6EfH5E597io7ebnBH/bVLPQ6L4zj9p6Ukjh6X76jX/Sczk3PXPPG6X3D1rp/Xa3Rhsm/MzP0LPJTXfoGO0X8ar/3ydHX0OYgL+dpnQDxBmbmt0SHmH4mIjYh4hqTna/QvS9PeLOn7IuJzI+IqSd8v6Y2NLRYn7jj9I+L5EfFZ9dudPF3SKyX9crMrxkmJiG5ErElakrQUEWsXOL/kzZJeGhFfHBGfJelm8bpfeJfbPyK+rj5PSRHxRRpd9ZDX/WL7WY1OF3heZu5c5HG89st0Wf157ZclIj4nIr4lIjYjYqm+UukLJf3mEQ9fzNd+ZvJxgh+SHi3p3ZK2JX1C0ovqzz9Loy2k48eFpNdK+nT98VpJ0fb6+Wis/9s1uuLhlkYnN7+y7bXz8Yi636LzVzEbf9wi6eq68dUTj/0+SZ+U9KCkX5C02vb6+Wimv6R/U7fflvRRjbaZLbe9fj5m7n5N3Xq37jz+eDGv/fI/jtOf135ZHxpdP+J9ku6vX89/Kull9deKeO1HvXAAAAAAgDm2mAIAAAAAJDEgAgAAAABqDIgAAAAAAEkMiAAAAACAGgMiAAAAAEASAyIAAAAAzK2IeENEfCoi/uwyHnt1RPxWRPxxRPxJRDz3uD8fAyIAAAssIt4eES84ge/z2Ij484hYPYl1AQBOzBslfe1lPvZmSe/IzKdK+hZJP3Pcn4wBEQCwUCLi4xGxExFbEx9Xtb2uNkTEl0p6iqRfru/fGBEfOOJxH4+Ir7nY98rMT0r6LUk3ncZaAQCzycz3S/r05Oci4okR8d8j4oMR8dsR8UXjh0u6or79mZLuOe7Px4AIAFhEz8vMzYmPh/0BGBHdNhbWsJdLeltm5gl9v7fV3xMAMN9eJ+m7M/Npkv65zh8pvEXSSyLibknvkfTdx/3GDIgAgCJExLURkRHx0oj4hKTfrD//FRHxuxFxf0TcHhHXT/yYx0fE+yLioYj4jYj49xHx1vpr19d/wE7+HAdH4iKiExGvioi/jIh7I+IdEfHoqbXcEBGfiIhzEfFDE99nKSJ+sP6xD9X/Avy4iPjpiPiJqZ/zVyPiey7wtL9O0vuO+et0+9TR15z4Nfnfkp4QEdcc53sCAJoTEZuS/oGk/xwR/0fSf5D0d+svv1DSGzPzrKTnSnpLRBxr5mNABACU5tmSniTpORHxuZJ+TdKtkh6t0b+yvjMiHlM/9hclfVDSlZJ+VNINx/h5XinpBfXPd5Wk+yT99NRjninpCyV9taQfjogn1Z//Po3+EH+uRluBvl1ST9KbJL1w/Id5RFxZ/9i3T//kEbEh6fGS/uIYa1ZmPmV85LVex19I+qP6awNJ/0+jbasAgPnUkXR/Zn7ZxMf4z5eXSnqHJGXm70la0+jPuGN9cwAAFs276yOC90fEu6e+dktmbmfmjqSXSHpPZr4nM6vM/A1Jt0l6bkRcLenvS3p1Zvbrczx+9RhreLmkH8rMuzOzr9G2nm+a2tr6mszcyczbJd2u84PXd0i6OTP/Ikduz8x7M/MPJD2g0VAojS4w8N76/MBpj6r/+9DU579i4tfm/oi4X9LV0z84Ip6p0eD8DZn54MSXHpr43gCAOVP/nv2xiPhmSYqR8Z8vn1D9Z0j9j5Jrkv72ON+fAREAsIhekJmPqj+mr+B518TtayR989Sw9EyNtuJcJem+zNyeePydx1jDNZJ+aeL7/rmkoaTHTjzmbyZu9yRt1rcfJ+kvL/B936TRYKv6v2+5wOPur//7GVOf//2JX5tHZeajNPoLw4GIeJxG/8J8Q2Z+ZOrHf8bE9wYAtCwi3i7p9yR9YUTcHREvlfRiSS+NiNsl3SHp+fXDv1/Sy+rPv13Sjcc9T93hBH4AgJfJPwjvkvSWzHzZ9IPq8+w+KyI2JobEqyd+/Lak9YnHL0l6zMS3uEvSt2fm7xzxva+9xBrvkvRESUe9p9VbJf1Z/a/BT5I0fYRUkpSZ2xHxl5K+QMf41+GIOFN/z5/MzP829bWupM/T6GgnAGAOZOYLL/Clh731RWZ+SNIzHsnPxxFEAEDJ3irpeRHxnPrCMGv1xWfOZuadGm03fU1ErNRbLp838WM/ImktIr4+IpY1em+pyfcI/DlJPza+oEtEPCYinq/L8/OSfjQiPr/eGvSlEfHZkpSZd0v6Q42OHL6z3ip7Ie/R6BzI43iDpA9n5muP+NrTJX28/rUBABhiQAQAFCsz79Jo280PanSU7S5JP6Dzf/69SNKXa/T+Uv9S0psnfuwDkr5Lo2HurzQ6ojh5VdOfkvQrkn49Ih6S9Pv197oc/1ajLZ6/LulBSa+XdGbi62+S9Pd04e2lY6+T9OKIiMv8eaXReY3/eOpKps+qv/ZijQZfAICpOLm3TgIAYLFFxC2SPi8zX3Kpx57yOr5Ko6Of12ZmdYnH/qKkd2TmkVtRj/Fzfo5Gb5nx1MzcfSTfCwCwuDgHEQCAOVJvZ/1nkn7+UsOhJGXmi07i583MT2l0ziMAwBhbTAEAmBP1Jcnv1+gqqz/Z8nIAAIbYYgoAAAAAkMQRRAAAAABAjQERAAAAACCJAREAAAAAUGNABAAAAABIYkAEAAAAANQYEAEAAAAAkqT/D2evhFjGZfFcAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xdcf2750>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "\n",
    "\n",
    "# Set the figure size\n",
    "\n",
    "plt.rcParams[\"figure.figsize\"] = (15, 10)\n",
    "\n",
    "\n",
    "\n",
    "# Display the results\n",
    "\n",
    "plt.plot(frequency, gamma)\n",
    "\n",
    "\n",
    "\n",
    "# Set the plot title and labels\n",
    "\n",
    "plt.title('Diffraction', size=14)\n",
    "\n",
    "plt.xlabel('Frequency (Hz)', size=12)\n",
    "\n",
    "plt.ylabel('Diffraction Loss (dB)', size=12)\n",
    "\n",
    "\n",
    "\n",
    "# Set the tick label size\n",
    "\n",
    "plt.tick_params(labelsize=12)\n",
    "\n",
    "\n",
    "\n",
    "# Turn on the grid\n",
    "\n",
    "plt.grid(linestyle=':', linewidth=0.5)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
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